Run LGG and HGG survival by splicing cluster assignment and splicing burden

Purpose:

Runs survival analysis models using splicing cluster assignment and 1) single exon splicing burden index (SBI) or 2) KEGG Spliceosome GSVA scores as a predictor

Usage

Uses a wrapper function (survival_analysis) from utils folder.

Setup

Packages and functions

Load packages, set directory paths and call setup script

library(tidyverse)
library(survival)
library(ggpubr)
library(ggplot2)
library(patchwork)

root_dir <- rprojroot::find_root(rprojroot::has_dir(\.git\))

data_dir <- file.path(root_dir, \data\)
analysis_dir <- file.path(root_dir, \analyses\, \survival\)
input_dir <- file.path(analysis_dir, \results\)
results_dir <- file.path(analysis_dir, \results\)
plot_dir <- file.path(analysis_dir, \plots\)

# If the input and results directories do not exist, create it
if (!dir.exists(results_dir)) {
  dir.create(results_dir, recursive = TRUE)
}

source(file.path(analysis_dir, \util\, \survival_models.R\))

Set metadata and cluster assignment file paths

metadata_file <- file.path(input_dir, \splicing_indices_with_survival.tsv\)

cluster_file <- file.path(root_dir, \analyses\,
                          \sample-psi-clustering\, \results\,
                          \sample-cluster-metadata-top-5000-events-stranded.tsv\)

kegg_scores_stranded_file <- file.path(root_dir, \analyses\,
                          \sample-psi-clustering\, \results\,
                          \gsva_output_stranded.tsv\)

Wrangle data Add cluster assignment and spliceosome gsva scores to metadata and define column lgg_group (LGG or non_LGG)

metadata <- read_tsv(metadata_file)
clusters <- read_tsv(cluster_file) %>%
  dplyr::rename(Kids_First_Biospecimen_ID = sample_id)
gsva_scores <- read_tsv(kegg_scores_stranded_file) %>%
  dplyr::filter(geneset == \KEGG_SPLICEOSOME\) %>%
  dplyr::rename(spliceosome_gsva_score = score)
# how many clusters?
n_clust <- length(unique(clusters$cluster))

metadata <- metadata %>%
  right_join(clusters %>% dplyr::select(Kids_First_Biospecimen_ID,
                                       cluster)) %>%
  left_join(gsva_scores %>% dplyr::select(sample_id,
                                          spliceosome_gsva_score),
            by = c(\Kids_First_Biospecimen_ID\ = \sample_id\)) %>% 
  dplyr::mutate(cluster = glue::glue(\Cluster {cluster}\)) %>%
  dplyr::mutate(cluster = fct_relevel(cluster,
                                               paste0(\Cluster \, 1:n_clust))) %>%
  dplyr::mutate(lgg_group = case_when(
    plot_group == \Low-grade glioma\ ~ \LGG\,
    TRUE ~ \non-LGG\
  )) %>%
  dplyr::mutate(SBI = SI_Total * 10) %>%
  dplyr::mutate(age_at_diagnosis_years = age_at_diagnosis_days/365.25)

Generate coxph models including extent of tumor resection, lgg group, and cluster assignment and SBI as covariates

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI\,
                               file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_SBI.RDS\),
                               \multivariate\,
                               years_col = \OS_years\,
                               status_col = \OS_status\)

forest_os <- plotForest(readRDS(file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_SBI.RDS\)))

forest_os

ggsave(file.path(plot_dir, \forest_add_OS_resection_lgg_group_cluster_assignment_SBI.pdf\),
       forest_os,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI\,
                               file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_SBI.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)

forest_efs <- plotForest(readRDS(file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_SBI.RDS\)))

forest_efs

ggsave(file.path(plot_dir, \forest_add_EFS_resection_lgg_group_cluster_assignment_SBI.pdf\),
       forest_efs,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

repeat analysis, replacing SBI with KEGG spliceosome gsva score

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score\,
                               file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS\),
                               \multivariate\,
                               years_col = \OS_years\,
                               status_col = \OS_status\)

forest_os <- plotForest(readRDS(file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS\)))

forest_os

ggsave(file.path(plot_dir, \forest_add_OS_resection_lgg_group_cluster_assignment_spliceosome_score.pdf\),
       forest_os,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

models <- c(\spliceosome_gsva_score\, \SBI\)

for (each in models) {
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                                terms = paste0(\extent_of_tumor_resection+lgg_group+cluster*\, each, \+age_at_diagnosis_years\),
                                 file.path(results_dir, paste0(\cox_EFS_interaction_terms_resection_lgg_group_cluster_\, each, \.RDS\)),
                                 \multivariate\,
                                 years_col = \EFS_years\,
                                 status_col = \EFS_status\)
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0(\cox_EFS_interaction_terms_resection_lgg_group_cluster_\, each, \.RDS\))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0(\forest_int_EFS_resection_lgg_group_cluster_assignment_\, each, \.pdf\)),
         int_forest_efs,
         width = 10, height = 6, units = \in\,
         device = \pdf\)

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                                terms = paste0(\extent_of_tumor_resection+lgg_group+cluster*\, each, \+age_at_diagnosis_years\),
                                 file.path(results_dir, paste0(\cox_OS_interaction_terms_resection_lgg_group_cluster_\, each, \.RDS\)),
                                 \multivariate\,
                                 years_col = \OS_years\,
                                 status_col = \OS_status\)
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0(\cox_OS_interaction_terms_resection_lgg_group_cluster_\, each, \.RDS\))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0(\forest_int_OS_resection_lgg_group_cluster_assignment_\, each, \.pdf\)),
         int_forest_os,
         width = 10, height = 6, units = \in\,
         device = \pdf\)
  
}

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score\,
                               file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)

forest_efs <- plotForest(readRDS(file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS\)))

forest_efs

ggsave(file.path(plot_dir, \forest_add_EFS_resection_lgg_group_cluster_assignment_spliceosome_score.pdf\),
       forest_efs,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

Subset metadata for LGG, and only include clusters with >= 10 samples

lgg <- metadata %>%
  dplyr::filter(plot_group == \Low-grade glioma\) %>%
  dplyr::mutate(cluster = factor(cluster)) %>%
  dplyr::mutate(mol_sub_group = fct_relevel(mol_sub_group, \Wildtype\, after = 0))

retain_clusters_lgg <- lgg %>%
  dplyr::count(cluster) %>%
  filter(n >= 10) %>%
  pull(cluster)

lgg <- lgg %>%
  filter(cluster %in% retain_clusters_lgg) %>%
    dplyr::mutate(cluster = factor(cluster))

Generate coxph models including covariates extent_of_tumor_resection, mol_sub_group, cluster, and SBI and plot

# identify LGG clusters
lgg_clusters <- metadata %>%
  filter(lgg_group == \LGG\) %>%
  mutate(cluster = as.integer(gsub(\cluster\, \\, cluster))) %>%
  pull(cluster) %>%
  sort() %>%
  unique()
add_model_lgg_efs <- fit_save_model(lgg[!lgg$cluster %in% lgg_clusters & !lgg$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+mol_sub_group+cluster+age_at_diagnosis_years+SBI\,
                               file.path(results_dir, \cox_lgg_EFS_additive_terms_resection_subtype_cluster_SBI.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)
forest_lgg_efs <- plotForest(readRDS(file.path(results_dir, \cox_lgg_EFS_additive_terms_resection_subtype_cluster_SBI.RDS\)))

forest_lgg_efs

ggsave(file.path(plot_dir, \forest_add_EFS_LGG_resection_subtype_cluster_assignment_SBI.pdf\),
       forest_lgg_efs,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

repeat analysis replacing SBI with spliceosome_gsva_score

add_model_lgg_efs <- fit_save_model(lgg[!lgg$cluster %in% lgg_clusters & !lgg$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+mol_sub_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score\,
                               file.path(results_dir, \cox_lgg_EFS_additive_terms_resection_subtype_cluster_spliceosome_score.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)
forest_lgg_efs <- plotForest(readRDS(file.path(results_dir, \cox_lgg_EFS_additive_terms_resection_subtype_cluster_spliceosome_score.RDS\)))

forest_lgg_efs

ggsave(file.path(plot_dir, \forest_add_EFS_LGG_resection_subtype_cluster_assignment_spliceosome_score.pdf\),
       forest_lgg_efs,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

Subset metadata for HGG and retain cluster with n >= 10

hgg <- metadata %>%
  dplyr::filter(plot_group %in% c(\Other high-grade glioma\, \Diffuse midline glioma\)) %>%
  dplyr::mutate(cluster = factor(cluster)) %>%
  dplyr::mutate(mol_sub_group = fct_relevel(mol_sub_group, \HGG

Generate HGG KM models with spliceosome_group as covariate

# Generate kaplan meier survival models for OS and EFS, and save outputs
hgg_kap_os <- survival_analysis(
  metadata  = hgg %>% dplyr::filter(!is.na(spliceosome_group)),
  ind_var = \spliceosome_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \OS_days\,
  status_col = \OS_status\
)
readr::write_rds(hgg_kap_os,
                 file.path(results_dir, \logrank_hgg_OS_splice_group.RDS\))

hgg_kap_efs <- survival_analysis(
  metadata  = hgg %>% dplyr::filter(!is.na(spliceosome_group)),
  ind_var = \spliceosome_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \EFS_days\,
  status_col = \EFS_status\
)
readr::write_rds(hgg_kap_efs,
                 file.path(results_dir, \logrank_hgg_EFS_splice_group.RDS\))

Generate KM plots

km_hgg_os_plot <- plotKM(model = hgg_kap_os,
                    variable = \spliceosome_group\,
                    combined = F, 
                    title = \HGG
ggsave(file.path(plot_dir, \km_hgg_OS_spliceosome_score.pdf\),
       km_hgg_os_plot,
       width = 9, height = 5, units = \in\,
       device = \pdf\)
km_hgg_efs_plot <- plotKM(model = hgg_kap_efs,
                    variable = \spliceosome_group\,
                    combined = F, 
                    title = \HGG
ggsave(file.path(plot_dir, \km_hgg_EFS_spliceosome_score.pdf\), 
       km_hgg_efs_plot,
       width = 9, height = 5, units = \in\,
       device = \pdf\)

Generate coxph models for HGG including covariates mol_sub_group cluster, and SBI, and plot

add_model_hgg_os <- fit_save_model(hgg,
                              terms = \mol_sub_group+age_at_diagnosis_years+SBI\,
                               file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_SBI.RDS\),
                               \multivariate\,
                               years_col = \OS_years\,
                               status_col = \OS_status\)
forest_hgg_os <- plotForest(readRDS(file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_SBI.RDS\)))

forest_hgg_os

ggsave(file.path(plot_dir, \forest_add_OS_HGG_subtype_cluster_assignment_SBI.pdf\),
       forest_hgg_os,
       width = 9, height = 5, units = \in\,
       device = \pdf\)

add_model_hgg_efs <- fit_save_model(hgg,
                              terms = \mol_sub_group+age_at_diagnosis_years+SBI\,
                               file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_SBI.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)

forest_hgg_efs <- plotForest(readRDS(file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_SBI.RDS\)))

ggsave(file.path(plot_dir, \forest_add_EFS_HGG_subtype_cluster_assignment_SBI.pdf\),
       forest_hgg_efs,
       width = 9, height = 5, units = \in\,
       device = \pdf\)

Repeat analysis replacing SBI with spliceosome_gsva_score

add_model_hgg_os <- fit_save_model(hgg,
                              terms = \mol_sub_group+age_at_diagnosis_years+spliceosome_gsva_score\,
                               file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_spliceosome_score.RDS\),
                               \multivariate\,
                               years_col = \OS_years\,
                               status_col = \OS_status\)
forest_hgg_os <- plotForest(readRDS(file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_spliceosome_score.RDS\)))

forest_hgg_os

ggsave(file.path(plot_dir, \forest_add_OS_HGG_subtype_cluster_assignment_spliceosome_score.pdf\),
       forest_hgg_os,
       width = 9, height = 5, units = \in\,
       device = \pdf\)

add_model_hgg_efs <- fit_save_model(hgg,
                              terms = \mol_sub_group+age_at_diagnosis_years+spliceosome_gsva_score\,
                               file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)

forest_hgg_efs <- plotForest(readRDS(file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS\)))

ggsave(file.path(plot_dir, \forest_add_EFS_HGG_subtype_cluster_assignment_spliceosome_score.pdf\),
       forest_hgg_efs,
       width = 9, height = 5, units = \in\,
       device = \pdf\)

Filter for cluster 7

cluster7_df <- metadata %>%
  dplyr::filter(cluster == \Cluster 7\,
                !is.na(EFS_days)) %>%
  dplyr::mutate(SI_group = case_when(
      SBI > summary(SBI)[\3rd Qu.\] ~ \High SBI\,
      SBI < summary(SBI)[\1st Qu.\] ~ \Low SBI\,
      TRUE ~ NA_character_
    )) %>%
  dplyr::mutate(spliceosome_group = case_when(
      spliceosome_gsva_score > summary(spliceosome_gsva_score)[\3rd Qu.\] ~ \Splice GSVA 4th Q\,
      spliceosome_gsva_score > summary(spliceosome_gsva_score)[\Median\] ~ \Splice GSVA 3rd Q\,
      spliceosome_gsva_score > summary(spliceosome_gsva_score)[\1st Qu.\] ~ \Splice GSVA 2nd Q\,
      TRUE ~ \Splice GSVA 1st Q\
    )) %>%
  dplyr::mutate(SI_group = fct_relevel(SI_group,
                                                 c(\High SBI\, \Low SBI\))) %>%
  dplyr::mutate(spliceosome_group = fct_relevel(spliceosome_group,
                                                 c(\Splice GSVA 1st Q\, 
                                                   \Splice GSVA 2nd Q\, 
                                                   \Splice GSVA 3rd Q\,
                                                   \Splice GSVA 4th Q\)))

Generate KM models with SI_group as covariate

# Generate kaplan meier survival models for OS and EFS, and save outputs
c7_si_kap_os <- survival_analysis(
  metadata  = cluster7_df %>% dplyr::filter(!is.na(SI_group)),
  ind_var = \SI_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \OS_days\,
  status_col = \OS_status\
)
readr::write_rds(c7_si_kap_os,
                 file.path(results_dir, \logrank_cluster7_OS_SBI.RDS\))

c7_si_kap_efs <- survival_analysis(
  metadata  = cluster7_df %>% dplyr::filter(!is.na(SI_group)),
  ind_var = \SI_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \EFS_days\,
  status_col = \EFS_status\
)
readr::write_rds(c7_si_kap_efs,
                 file.path(results_dir, \logrank_cluster7_EFS_SBI.RDS\))

Generate Cluster 7 KM SI_group plots

km_c7_si_os_plot <- plotKM(model = c7_si_kap_os,
                    variable = \SI_group\,
                    combined = F, 
                    title = \Cluster 7
ggsave(file.path(plot_dir, \km_cluster7_OS_sbi_group.pdf\),
       km_c7_si_os_plot,
       width = 8, height = 5, units = \in\,
       device = \pdf\)
km_c7_si_efs_plot <- plotKM(model = c7_si_kap_efs,
                    variable = \SI_group\,
                    combined = F, 
                    title = \Cluster 7
ggsave(file.path(plot_dir, \km_cluster7_EFS_sbi_group.pdf\), 
       km_c7_si_efs_plot,
       width = 8, height = 5, units = \in\,
       device = \pdf\)

Generate KM models with spliceosome_group as covariate

# Generate kaplan meier survival models for OS and EFS, and save outputs
c7_splice_kap_os <- survival_analysis(
  metadata  = cluster7_df %>% 
    dplyr::filter(spliceosome_group %in% c(\Splice GSVA 4th Q\, \Splice GSVA 1st Q\)) %>%
    dplyr::mutate(spliceosome_group = factor(spliceosome_group,
                                                  levels = c(\Splice GSVA 1st Q\, \Splice GSVA 4th Q\))),
  ind_var = \spliceosome_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \OS_days\,
  status_col = \OS_status\
)
readr::write_rds(c7_splice_kap_os,
                 file.path(results_dir, \logrank_cluster7_OS_splice_group.RDS\))

c7_splice_kap_efs <- survival_analysis(
  metadata  = cluster7_df %>% 
    dplyr::filter(spliceosome_group %in% c(\Splice GSVA 4th Q\, \Splice GSVA 1st Q\)) %>%
    dplyr::mutate(spliceosome_group = factor(spliceosome_group,
                                                  levels = c(\Splice GSVA 1st Q\, \Splice GSVA 4th Q\))),
  ind_var = \spliceosome_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \EFS_days\,
  status_col = \EFS_status\
)
readr::write_rds(c7_splice_kap_efs,
                 file.path(results_dir, \logrank_cluster7_EFS_splice_group.RDS\))

Generate Cluster 7 KM spliceosome_group plots

km_c7_splice_os_plot <- plotKM(model = c7_splice_kap_os,
                    variable = \spliceosome_group\,
                    combined = F, 
                    title = \Cluster 7
ggsave(file.path(plot_dir, \km_cluster7_OS_splice_group.pdf\),
       km_c7_splice_os_plot,
       width = 9, height = 5, units = \in\,
       device = \pdf\)
km_c7_splice_efs_plot <- plotKM(model = c7_splice_kap_efs,
                    variable = \spliceosome_group\,
                    combined = F, 
                    title = \Cluster 7
ggsave(file.path(plot_dir, \km_cluster7_EFS_splice_group.pdf\), 
       km_c7_splice_efs_plot,
       width = 9, height = 5, units = \in\,
       device = \pdf\)

Assess EFS and OS by SBI or spliceosome GSVA score in multivariate models and generate forest plots

add_model_c7_efs <- fit_save_model(cluster7_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != \Unavailable\,
                                                    spliceosome_group %in% c(\Splice GSVA 4th Q\, \Splice GSVA 1st Q\)) %>%
                                      dplyr::mutate(plot_group = fct_relevel(plot_group, \Low-grade glioma\, after = 0)),
                              terms = \extent_of_tumor_resection+age_at_diagnosis_years+plot_group+spliceosome_group\,
                               file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)
forest_c7_spliceosome_efs <- plotForest(readRDS(file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS\)))

ggsave(file.path(plot_dir, \forest_add_EFS_cluster7_histology_resection_spliceosome_group.pdf\),
       forest_c7_spliceosome_efs,
       width = 9, height = 4, units = \in\,
       device = \pdf\)

add_model_c7_os <- fit_save_model(cluster7_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\)) %>%
                                    dplyr::mutate(plot_group = fct_relevel(plot_group, \Low-grade glioma\, after = 0)),
                              terms = \extent_of_tumor_resection+age_at_diagnosis_years+plot_group+SBI\,
                               file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_si_group.RDS\),
                               \multivariate\,
                               years_col = \OS_years\,
                               status_col = \OS_status\)
forest_c7_si_os <- plotForest(readRDS(file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_si_group.RDS\)))

ggsave(file.path(plot_dir, \forest_add_OS_cluster7_histology_resection_si.pdf\),
       forest_c7_si_os,
       width = 9, height = 4, units = \in\,
       device = \pdf\)

Print session info

sessionInfo()

R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gtools_3.9.5    survminer_0.4.9 patchwork_1.2.0 ggpubr_0.6.0   
 [5] survival_3.7-0  lubridate_1.9.4 forcats_1.0.1   stringr_1.6.0  
 [9] dplyr_1.1.4     purrr_1.2.0     readr_2.1.6     tidyr_1.3.1    
[13] tibble_3.3.0    ggplot2_4.0.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.54          bslib_0.9.0        rstatix_0.7.2     
 [5] lattice_0.22-7     tzdb_0.5.0         vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.4     parallel_4.4.0     pkgconfig_2.0.3    Matrix_1.7-4      
[13] data.table_1.17.8  RColorBrewer_1.1-3 S7_0.2.1           lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       textshaping_1.0.4  carData_3.0-5     
[21] colorblindr_0.1.0  htmltools_0.5.8.1  sass_0.4.10        yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.11.1      car_3.1-2          jquerylib_0.1.4   
[29] cachem_1.1.0       abind_1.4-5        km.ci_0.5-6        commonmark_2.0.0  
[33] tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7      labeling_0.4.3    
[37] splines_4.4.0      cowplot_1.1.3      rprojroot_2.1.1    fastmap_1.2.0     
[41] grid_4.4.0         colorspace_2.1-2   cli_3.6.5          magrittr_2.0.4    
[45] broom_1.0.10       withr_3.0.2        scales_1.4.0       backports_1.5.0   
[49] bit64_4.6.0-1      timechange_0.3.0   rmarkdown_2.30     ggtext_0.1.2      
[53] bit_4.6.0          gridExtra_2.3      ggsignif_0.6.4     ragg_1.5.0        
[57] zoo_1.8-12         hms_1.1.4          evaluate_1.0.5     knitr_1.50        
[61] KMsurv_0.1-5       markdown_1.13      survMisc_0.5.6     rlang_1.1.6       
[65] Rcpp_1.1.0         gridtext_0.1.5     xtable_1.8-4       glue_1.8.0        
[69] xml2_1.5.0         vroom_1.6.6        jsonlite_2.0.0     R6_2.6.1          
[73] systemfonts_1.3.1